Typically, the business knowledge and user terminology of an enterprise are distributed throughout an entire company, in the way the employees speak to one another, and in the many documents of the company.
Business software applications used by enterprises are built from business objects that group/encapsulate the definition of business terminology according to relevant content information (e.g. attributes defining business data which are described by underlying global data type) used by the application. For example, a defined business object, such as a material business object provides business-related terminology, such as the definition of the material (e.g., medium-density fiberboard in a home improvement company) and the material names (e.g., MDF) used/defined in a particular company. In addition, the acronym “MDF” may also refer to a product, such as a “metallic dual faucet.” In this situation, there are different subject matter categories. In the particular instance, there is a product category, and sub-categories for wood products, (e.g., the medium density fiberboard), and plumbing products (e.g., the metallic dual faucet).
In addition, in large enterprises, a term may not have the same acronym from one division to the next, or, as above, the acronym may have an entirely different definition. Furthermore, an accounting department may have similar terms or acronyms as a sales department. A common problem is how to detect, and determine the business terminology being used within all divisions of the company, and how to consolidate it in a category-oriented data structure.
A challenge to accomplishing the indexing of data values is how to detect and determine the business terminology that is used in the everyday vernacular of the enterprise, and then consolidating the determined business terminology in respective categories. The consolidating of the business terminology into categories for a specific business may be developed manually by populating a database with the specific business terms and their definitions. However, such a manual approach is time consuming and costly.
An existing solution allows only the import of preconfigured semantic terminology, or the manual creation and/or adaption of information stored in a semantic network having domains, terms and term types.